Code for training networks.
- das.train.train(*, data_dir: str, y_suffix: str = '', save_dir: str = './', save_prefix: Optional[str] = None, model_name: str = 'tcn', nb_filters: int = 16, kernel_size: int = 16, nb_conv: int = 3, use_separable: List[bool] = False, nb_hist: int = 1024, ignore_boundaries: bool = True, batch_norm: bool = True, nb_pre_conv: int = 0, pre_nb_dft: int = 64, pre_kernel_size: int = 3, pre_nb_filters: int = 16, pre_nb_conv: int = 2, nb_lstm_units: int = 0, verbose: int = 2, batch_size: int = 32, nb_epoch: int = 400, learning_rate: Optional[float] = None, reduce_lr: bool = False, reduce_lr_patience: int = 5, fraction_data: Optional[float] = None, seed: Optional[int] = None, batch_level_subsampling: bool = False, tensorboard: bool = False, neptune_api_token: Optional[str] = None, neptune_project: Optional[str] = None, log_messages: bool = False, nb_stacks: int = 2, with_y_hist: bool = True, x_suffix: str = '', balance: bool = False, version_data: bool = True, _qt_progress: bool = False) Tuple[tensorflow.python.keras.engine.training.Model, Dict[str, Any]] ¶
Train a DeepSS network.
data_dir (str) – Path to the directory or file with the dataset for training. Accepts npy-dirs (recommended), h5 files or zarr files. See documentation for how the dataset should be organized.
y_suffix (str) – Select training target by suffix. Song-type specific targets can be created with a training dataset, Defaults to ‘’ (will use the standard target ‘y’)
save_dir (str) – Directory to save training outputs. The path of output files will constructed from the SAVE_DIR, an optional prefix, and the time stamp of the start of training. Defaults to current directory (‘./’).
save_prefix (Optional[str]) – Prepend to timestamp. Name of files created will be SAVE_DIR/SAVE_PREFIX + “_” + TIMESTAMP or SAVE_DIR/ TIMESTAMP if SAVE_PREFIX is empty. Defaults to ‘’ (empty).
model_name (str) – Network architecture to use. Use “tcn” (TCN) or “tcn_stft” (TCN with STFT frontend). See das.models for a description of all models. Defaults to ‘tcn’.
nb_filters (int) – Number of filters per layer. Defaults to 16.
kernel_size (int) – Duration of the filters (=kernels) in samples. Defaults to 16.
nb_conv (int) – Number of TCN blocks in the network. Defaults to 3.
use_separable (List[bool]) – Specify which TCN blocks should use separable convolutions. Provide as a space-separated sequence of “False” or “True. For instance: “True False False” will set the first block in a three-block (as given by nb_conv) network to use separable convolutions. Defaults to False (no block uses separable convolution).
nb_hist (int) – Number of samples processed at once by the network (a.k.a chunk size). Defaults to 1024.
ignore_boundaries (bool) – Minimize edge effects by discarding predictions at the edges of chunks. Defaults to True.
batch_norm (bool) – Batch normalize. Defaults to True.
nb_pre_conv (int) – Downsampling rate. Adds downsampling frontend if not 0. TCN_TCN: adds a frontend of N conv blocks (conv-relu-batchnorm-maxpool2) to the TCN. TCN_STFT: adds a trainable STFT frontend. Defaults to 0 (no frontend).
pre_nb_dft (int) – Number of filters (roughly corresponding to filters) in the STFT frontend. Defaults to 64.
pre_nb_filters (int) – Number of filters per layer in the pre-processing TCN. Defaults to 16.
pre_kernel_size (int) – Duration of filters (=kernels) in samples in the pre-processing TCN. Defaults to 3.
nb_lstm_units (int) – If >0, adds LSTM with given number of units to the output of the stack of TCN blocks. Defaults to 0 (no LSTM layer).
verbose (int) – Verbosity of training output (0 - no output(?), 1 - progress bar, 2 - one line per epoch). Defaults to 2.
batch_size (int) – Batch size Defaults to 32.
nb_epoch (int) – Maximal number of training epochs. Training will stop early if validation loss did not decrease in the last 20 epochs. Defaults to 400.
learning_rate (Optional[float]) – Learning rate of the model. Defaults should work in most cases. Values typically range between 0.1 and 0.00001. If None, uses per model defaults: “tcn” 0.0001, “tcn_stft” 0.0005). Defaults to None.
reduce_lr (bool) – Reduce learning rate on plateau. Defaults to False.
reduce_lr_patience (int) – Number of epochs w/o a reduction in validation loss after which to trigger a reduction in learning rate. Defaults to 5.
fraction_data (Optional[float]) – Fraction of training and validation to use for training. Defaults to 1.0.
seed (Optional[int]) – Random seed to reproducible select fractions of the data. Defaults to None (no seed).
batch_level_subsampling (bool) – Select fraction of data for training from random subset of shuffled batches. If False, select a continuous chunk of the recording. Defaults to False.
tensorboard (bool) – Write tensorboard logs to save_dir. Defaults to False.
neptune_api_token (Optional[str]) – API token for logging to neptune.ai. Defaults to None (no logging).
neptune_project (Optional[str]) – Project to log to for neptune.ai. Defaults to None (no logging).
log_messages (bool) – Sets logging level to INFO. Defaults to False (will follow existing settings).
nb_stacks (int) – Unused if model name is “tcn” or “tcn_stft”. Defaults to 2.
with_y_hist (bool) – Unused if model name is “tcn” or “tcn_stft”. Defaults to True.
x_suffix (str) – Select specific training data based on suffix (e.g. x_suffix). Defaults to ‘’ (will use the standard data ‘x’)
balance (bool) – Balance data. Weights class-wise errors by the inverse of the class frequencies. Defaults to False.
version_data (bool) – Save MD5 hash of the data_dir to log and params.yaml. Defaults to True (set to False for large datasets since it can be slow).
Returns – model (keras.Model) params (Dict[str, Any])